64%
Amazon Ads value occurs OFF Amazon
33%
purchases show brand halo beyond category
9/10
brands see higher CVR after TV exposure
$6.5M+
Pablo's annual ad spend under management
Executive Summary
The Measurement Gap No One Is Closing
Bayesian Media Mix Modeling (MMM) is the gold standard for understanding how advertising spend across channels creates halo effects โ the invisible lift that Amazon Ads, TV, and social media create in DTC, Walmart, and retail channels that traditional attribution completely misses.
"64% of Amazon Ads-attributed purchases occur off-Amazon. 33% of those show brand halo effects beyond the promoted category." โ Amazon Ads x OCM Case Study
Pablo Champion has managed $6.5M+ in annual Amazon ad spend across 34 marketplaces. He has personally felt this measurement gap. This is not an abstract idea โ it is the specific problem his clients cannot solve.
๐ด Social Graph โ WHO
Key Players & Market Structure
Enterprise platforms (serving $50M+ ad spend brands):
- Google Meridian โ open-source Bayesian MMM framework (2024), Google's move to standardize the field
- Meta Robyn โ open-source, Python/R, Meta's cross-platform attribution answer
- Measured.com โ incrementality-first, strong in DTC eCommerce
- Sellforte โ real-time MMM for eCommerce, daily refresh capability
- Recast โ statistical MMM challenger, transparent methodology
- Nielsen & IRI โ legacy players, enterprise CPG focus only
The structural gap: Most platforms serve enterprise brands with $50M+ in annual ad spend. Mid-market Amazon-native brands ($500Kโ$10M) have no purpose-built solution. Agencies like My Amazon Guy and PTA manage these brands manually, with zero MMM infrastructure.
Amazon's own OCM (Omnichannel Metrics) provides some cross-channel data โ but only for DSP buyers, with no integration into broader MMM frameworks.
๐ต Knowledge Graph โ WHAT
Hard Data & Validated Evidence
64%
of Amazon Ads-attributed purchases occur off Amazon โ invisible to standard attribution
33%
of off-Amazon sales show brand halo โ driving sales beyond directly promoted categories
60%
of companies see conversion rate lift of 50%+ when cross-channel halo is modeled
100%+
CVR lift seen by 33% of brands when upper-funnel halo effects are properly attributed
Why Bayesian vs. Frequentist MMM?
- Frequentist: "TV adds exactly $2.5M" โ a point estimate with hidden uncertainty
- Bayesian: "Most likely $2.0Mโ$3.0M, with quantified probability distribution" โ defensible budget decisions
The math that matters (Adstock):
Adstock(t) = Spend(t) + ฮป ร Adstock(t-1)
Saturation: f(x) = log(1 + x)
ฮป (lambda) controls the decay rate โ TV advertising has a much longer tail than digital display. Saturation modeling captures diminishing returns as spend scales up. Both are invisible in standard platform reporting.
Data requirements for a functional MMM: 3+ years of historical data, all channels (digital + traditional + paid + earned), and control variables (seasonality, promotions, competitor activity, weather, economic conditions).
๐ข Generative Graph โ WHAT IF
Pablo's Strategic Wedge
Pablo's unfair advantage is rare: he is simultaneously a practitioner (managing $6.5M+ in live Amazon ad spend) and a builder (custom Manus AI Buy Box scraper). He understands the measurement gap from the inside โ not from a whitepaper.
๐ฏ Venture Concept: "HaloIQ" (working name)
- Core Product: Bayesian MMM engine purpose-built for Amazon-native, multi-marketplace brands
- Data Inputs: Amazon Ads API + Walmart Connect + Instacart + Meta + Google + Shopify/DTC revenue
- Output: Cross-channel halo attribution dashboard โ which spend is "halo-creating" vs. "siloed"
- Monetization: SaaS ($2Kโ$5K/month for mid-market brands) or agency white-label
- Distribution: Pablo already manages 9 accounts across 34 marketplaces โ these are beta customers today
Runes activated for this opportunity:
โ Insight
โ Grounding
โ Risk
โ Praxis
โฆ Imagination
The contrarian stress-test:
If 64% of Amazon Ads value is off-Amazon, why haven't Amazon's own tools closed this gap? Is it a technical limitation โ or a business model conflict of interest? Amazon profits from brands optimizing inside the walled garden. This asymmetry may be Pablo's most durable structural moat.
Kill criteria (โ Risk): Amazon expands OCM to include MMM-grade cross-channel modeling at no cost to DSP buyers. Monitor Amazon Ads product roadmap quarterly as a kill signal.
โ Praxis โ Next Steps
What Pablo Does Next
1
Run a manual Bayesian MMM for one existing PTA client โ use
Google Meridian (open-source) or
PyMC-Marketing. Target a brand with Amazon + DTC + Walmart presence. This is your proof of concept.
2
Document the output as a case study โ this becomes the anchor node of your Knowledge Graph and the proof point for every investor or partner conversation.
3
Map the competitive landscape precisely โ where are Measured.com and Recast's pricing floors? What segments do they explicitly not serve? Find the floor of the market they ignore.
4
Build your '-ity' vocabulary โ define the 15 words ending in '-ity' that will become the brand ethos of this venture. Clarity, Accountability, Reciprocity... what else?
5
Activate the Social Graph โ who in Pablo's existing network (brand owners, agency leads, Amazon category managers) would pay for this product today if it existed?
Sources & Citations
Research Foundation